Eecient Algorithms for Atmospheric Correction of Remotely Sensed Data

Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics such as global carbon modeling, biogeochemical cycling, hydrological modeling, and ecosystem response mod-eling. However, the large amounts of imagery collected by the satellites are largely contaminated by the eeects of atmospheric particles through absorption and scattering of the radiation from the earth surface. The objective of atmospheric correction is to retrieve the surface reeectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric eeects. Atmospheric correction has been shown to signiicantly improve the accuracy of image classiication. This problem has received a considerable attention from researchers in remote sensing who have devised a number of solution approaches. Sophisticated approaches are computationally demanding and have only been validated on 1 a very small scale. We introduce a number of computational techniques that lead to a substantial speedup of an atmospheric correction algorithm based on using look-up tables that are generated from radiative transfer computations. Excluding I/O time, the previous known implementation processes one pixel at a time and requires about 2.63 seconds per pixel on a SPARC-10 machine, while our implementation is based on processing the whole image and takes about 4-20 microseconds per pixel on the same machine. We also develop a parallel version of our algorithm that is scalable in terms of both computation and I/O. Experimental results obtained show that a Thematic Mapper (TM) image (36 MB per band, 5 bands need to be corrected) can be handled in less than 4.3 minutes on a 32-node CM-5 machine, including I/O time.